提出两种新方法，让AI更准识别未知深伪视频。
Enhancing Abnormality Identification: Robust Out-of-Distribution Strategies for Deepfake Detection
- 用图像重建+注意力机制，识别不在训练数据中的深伪内容。
- 在基准测试中表现顶尖，对未知生成模型仍有效。
- 适合需要实时应对新型深伪技术的安防系统使用。
深度伪造检测已成为计算机视觉与人工智能中的关键挑战。尽管检测技术取得显著进展，但在开放集场景下的泛化能力仍是长期难题。神经网络通常基于闭世界假设训练，但新型生成模型不断涌现，导致不可避免地遇到训练分布外的数据。为此，本文提出两种新的分布外（OOD）检测方法：第一种通过重构输入图像来识别异常，第二种引入注意力机制增强检测能力。实验表明，所提方法在性能上优于现有先进技术，在基准测试中位列前列，展现出在动态、真实场景中构建鲁棒、可适应解决方案的巨大潜力。
原文摘要 · Abstract (English)
Detecting deepfakes has become a critical challenge in Computer Vision and Artificial Intelligence. Despite significant progress in detection techniques, generalizing them to open-set scenarios continues to be a persistent difficulty. Neural networks are often trained on the closed-world assumption, but with new generative models constantly evolving, it is inevitable to encounter data generated by models that are not part of the training distribution. To address these challenges, in this paper, we propose two novel Out-Of-Distribution (OOD) detection approaches. The first approach is trained to reconstruct the input image, while the second incorporates an attention mechanism for detecting OODs. Our experiments validate the effectiveness of the proposed approaches compared to existing state-of-the-art techniques. Our method achieves promising results in deepfake detection and ranks among the top-performing configurations on the benchmark, demonstrating their potential for robust, adaptable solutions in dynamic, real-world applications.
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